Competitor menu benchmarking
Compare menu structure, item breadth, variants, and visible add-ons across an agreed competitor set.
UK food-delivery marketplace data
Just Eat Data Scraping Services for UK Restaurant, Menu & Price Intelligence
Structured Just Eat restaurant, menu, pricing and delivery-context data with the postcode and observation time kept attached
NenoData scopes managed Just Eat UK data projects for restaurant, pricing, market-intelligence, and data teams that need marketplace information in a consistent dataset rather than disconnected restaurant exports.
Start with the restaurants, UK postcodes or locations, menu depth, pricing fields, delivery signals, refresh requirements, and output structure you need. NenoData reviews the approved source and representative pages first, confirms field feasibility, and aligns a sample before production.
Depending on what is publicly visible and confirmed during scoping, a Just Eat dataset may include restaurant identity, menu categories and items, listed prices, variants or add-ons, promotion text, delivery fees, minimum-order information, ratings or review counts, availability signals, location context, source metadata, and observation timestamps.
- UK postcode/location context
- Sample-first source validation
- Menu + price-component structure
- Structured delivery where scoped
Why postcode, location and timestamp context matter for Just Eat data
Just Eat is a location-dependent marketplace.
Its UK site tells users where they are so it can show nearby stores and restaurants, and its location pages direct users to enter a postcode to see local options. Brand pages likewise indicate that participating restaurants and estimated delivery times depend on the user's address or postcode.
That means a restaurant or menu observation is more useful when the dataset keeps the location input and collection time attached.
Without that context, an analyst can easily compare two different branches of the same chain, menus captured for different delivery areas, or prices captured in different observation windows.
The objective is not simply to export restaurant pages. It is to preserve enough context for later comparisons to remain defensible.
- Source
- Postcode/location input
- Restaurant
- Menu/category/item
- Observed price or delivery signal
- Timestamp
What Just Eat restaurant data may be collected?
The final schema depends on the approved Just Eat domain, page type, location input, and representative sample.
Potential fields include:
Restaurant and location
- Restaurant name
- Source URL or source identifier where available
- Restaurant address where displayed
- City
- Postcode/location input
- Cuisine or marketplace category
Delivery and commercial context
- Delivery/collection availability
- Minimum-order value or no-minimum state
- Delivery fee where displayed
- Service-fee text where displayed
- Small-order-fee text where displayed
- Promotion or offer text
Reputation and lineage
- Rating where visible
- Review count where visible
- Estimated delivery-time context where visible
- Collection timestamp
Visible fee components should remain separate. A listing-page delivery fee should not automatically be represented as a final checkout total.
Just Eat menu data scraping
Restaurant-level extraction answers questions such as which restaurants are visible, how they are categorized, and what rating, delivery, or minimum-order signals are displayed.
Menu-level extraction goes deeper.
Depending on the representative pages and agreed scope, menu records may include:
- Menu category
- Item name
- Item description
- Listed item price
- Currency
- “From” price
- Size or variant where visible
- Modifier/add-on label where exposed
- Modifier/add-on price where displayed
- Bundle or meal-deal text
- Promotion text
- Availability status
- Dietary or item tags where shown
- Source URL
- Restaurant/location context
- Collection timestamp
Just Eat UK pages publicly expose menu categories, items, GBP prices, “from” prices, configurable products, promotions, and availability states. Modifier depth still needs to be tested on representative target pages.
The Just Eat pricing components that should stay separate
Menu
Listed menu-item price
The visible GBP price attached to an item at the location and time of collection.
Variant or starting price
Some menu items use “from” pricing, meaning a final configured item may have more than one price.
Modifier or add-on price
Where the public source exposes paid extras, retain the extra amount separately from the item price.
Promotion
Promotion context
Capture the visible promotion, qualification, or bundle language rather than assuming a normalized discounted unit price.
Delivery
Delivery fee
Keep the displayed delivery fee as its own observed field.
Service fee and small-order fee
Where displayed, retain these as separate components.
Minimum order
Store this separately from fees. Some restaurants display no minimum; others display a threshold.
Context on every component:Postcode/locationTimestamp
Final checkout total
Do not infer the final checkout amount simply by summing visible listing components unless that total is separately observed and validated.
Visible components ≠ automatically the final checkout total
Illustrative Just Eat dataset structure
Illustrative schema — actual fields confirmed during scoping
| Collected at | Postcode input | Restaurant | Category | Item | Listed price | Promotion | Delivery fee | Min. order | Review count |
|---|---|---|---|---|---|---|---|---|---|
| 2026-09-25 10:30 UTC | Example postcode | Example restaurant | Example category | Example item | Example GBP value | Example offer | Example value | Example value/state | Example count |
{
"collection_timestamp": "2026-09-25T10:30:00Z",
"source_name": "Just Eat UK",
"source_url": "example_public_source_url",
"postcode_input": "Example postcode",
"restaurant_name": "Example restaurant",
"restaurant_source_id": "example_id_if_available",
"restaurant_address": "Example displayed address",
"city": "Example city",
"menu_category": "Example category",
"item_name": "Example item",
"item_source_id": "example_id_if_available",
"item_description": "Example description",
"listed_price": "Example value",
"currency": "GBP",
"variant_label": "Example size or option",
"modifier_group": "Example modifier group",
"modifier_name": "Example add-on",
"modifier_price": "Example value",
"promotion_text": "Example offer",
"delivery_fee": "Example value",
"service_fee": "Example value",
"small_order_fee": "Example value",
"minimum_order": "Example value or no-minimum state",
"availability_status": "Example status",
"estimated_delivery_time": "Example displayed ETA",
"rating": "Example visible rating",
"review_count": "Example visible count",
"validation_status": "Example QA state"
}Outputs can be scoped for CSV, Excel, JSON, API-ready records, scheduled feeds, or cloud/database delivery where the selected method is technically feasible and agreed.
Monitoring Just Eat menu prices over time
A price-monitoring project needs identity + context + timestamp, not simply repeated page exports.
- 1
Establish the restaurant key
Use source identifiers where available. Otherwise, combine agreed fields such as source URL, restaurant name, address/location, and postcode context.
- 2
Establish the menu-item key
Matching may consider a source item identifier, restaurant/location key, category, raw item name, normalized name, variant, and modifier context.
- 3
Preserve the raw observation
Keep original source labels alongside normalized values.
- 4
Keep timestamps
A price difference without observation times does not establish when a change occurred.
- 5
Use explicit match states
Where included in scope, keep exact, variant, comparable, unmatched, or review-required states visible rather than forcing every record into a false match.
- 6
Build prospective history
Recurring collection can create a history from the first agreed monitoring cycle onward.
Historical backfill from before the project starts should only be stated when separately verified.
Just Eat data scraping use cases
Competitor price monitoring
Track listed menu prices across the same restaurants and location contexts over repeated observation windows.
Promotion tracking
Monitor visible spend-threshold offers, percentage-off promotions, bundles, and other promotion text.
Delivery-fee comparison
Compare visible delivery, service, small-order, and minimum-order fields without collapsing them into one ambiguous fee.
Restaurant market mapping
Build restaurant lists around defined postcodes, cities, cuisines, or chain sets where the source supports the selected location inputs.
Cuisine/category research
Compare observed restaurant and menu coverage across defined locations without representing listing visibility as consumer demand.
Rating/reputation monitoring
Track public rating and review-count signals where visible.
Food-tech dataset enrichment
Add structured Just Eat restaurant, menu, price, postcode, and source-lineage fields to an internal dataset.
Multi-platform restaurant intelligence
Normalize comparable fields across multiple approved delivery marketplaces while keeping platform and source-specific fields attached.
Restaurant-level vs menu-level Just Eat extraction
| Question | Restaurant level | Menu level |
|---|---|---|
| Restaurant visibility | Yes | Context |
| Restaurant identity/location | Yes | Yes |
| Cuisine/category | Where shown | Context |
| Rating/review count | Where visible | Context |
| Minimum order | Where visible | Context |
| Delivery/fee signals | Where visible | Context |
| Menu categories | Limited | Yes |
| Individual items | Limited | Yes |
| Item descriptions | No/limited | Where shown |
| Item prices | No/limited | Yes |
| Variants/modifiers | No | Where visible |
| Item availability | No/limited | Where visible |
| Best for | Market mapping, reputation, delivery economics | Menu benchmarking and item-level price monitoring |
Just Eat-only monitoring or a multi-platform food-delivery dataset?
| Requirement | Just Eat-focused workflow | Multi-platform workflow |
|---|---|---|
| Main question | What is visible on the approved Just Eat source? | How do sources differ? |
| Location context | UK postcode/location | Platform + location |
| Menu structure | Preserve Just Eat hierarchy | Shared schema + source-specific fields |
| Prices | Compare Just Eat observations | Match equivalent items first |
| Fees | Preserve Just Eat labels | Preserve platform-specific semantics |
| Restaurant matching | Primarily within one source | Cross-platform matching may be required |
| Best route | Just Eat source page | Food Delivery App Scraping |
For a Just Eat + Deliveroo request:
- 1Confirm Just Eat source feasibility.
- 2Confirm Deliveroo scope.
- 3Define shared and source-specific fields.
- 4Define restaurant/item matching rules.
- 5Produce cross-platform comparisons only after matching is validated.
- Just Eat onlyleads to This page
- Just Eat + Deliverooleads to Confirm both sources + shared schema + matching
- Several marketplacesleads to Food Delivery App Scraping
How a NenoData Just Eat project would work
- 1
Share requirements
Provide target country/domain, UK postcodes or locations, restaurant list, menu depth, required pricing/delivery fields, refresh expectations, and desired output.
- 2Sample gate
Review source feasibility and collect approved fields
NenoData checks representative pages and confirms the field set before production.
- 3
Clean, normalize and validate
Records are mapped to the agreed schema, checked for completeness, deduplicated where applicable, and prepared for comparison.
- 4
Deliver the confirmed dataset or feed
Delivery can be scoped for CSV, Excel, JSON, API-ready structures, scheduled feeds, or cloud/database destinations where confirmed.
Data quality for valid Just Eat comparisons
Preserve location context
Do not compare rows from different postcodes as if they represent the same marketplace state.
Preserve restaurant identity
Keep source IDs/URLs where available and define explicit matching rules when they are not.
Preserve menu hierarchy
Maintain:
restaurant → category → item → variant/modifier
Separate price components
Do not merge menu price, modifier price, promotion, delivery fee, service fee, small-order fee, and minimum order.
Keep timestamps
Repeated observations need collection times.
Do not interpret missing as removed automatically
A missing item may reflect temporary unavailability, page-state change, source-layout change, a different location context, collection failure, or genuine removal.
Keep exceptions visible
Uncertain item matches should remain unresolved or review-required rather than producing false price changes.
Scope and boundaries
- Approved sources only
- Public, permissioned, or otherwise authorized sources and collection methods confirmed during scoping.
- UK first
- Broader country coverage requires individual brand/domain and feasibility review.
- No every-restaurant promise
- Coverage depends on location context and approved target set.
- No universal real-time claim
- Refresh cadence is confirmed for the project.
- Hourly requests require validation
- Hourly monitoring should be treated as a requested cadence until feasibility is confirmed.
- No private customer/order data
- Private, restricted, login-protected, order-history, or personal data is outside this marketplace service.
- No demand inference
- Marketplace visibility, prices, promotions, availability, ratings, and review counts do not establish actual order demand.
- Review text is not assumed
- Individual review availability requires separate validation.
- Modifier depth is scoped
- Base menu visibility does not guarantee every nested option.
- Historical backfill is not implied
- Recurring monitoring can build a prospective history; older historical coverage requires separate evidence.
Why NenoData is relevant for a Just Eat data project
Source-first scoping
NenoData's current site supports feasibility review and sample-first scoping for sources that are not already listed.
Menu-aware schema design
NenoData's restaurant-menu service supports structured categories, items, prices, modifiers, promotions, availability, location context, timestamps, and source metadata where scoped.
Explicit match and change logic
NenoData's price-intelligence workflows support explicit comparison and lifecycle states rather than forcing uncertain records into matches.
Managed data delivery
Comparable NenoData marketplace workflows support structured files and pipeline-oriented delivery where scoped.
Multi-platform path
Projects can route into NenoData's food-delivery and Deliveroo workflows when multiple approved marketplaces are required.
Managed service, not a self-service scraper claim
This page describes a managed collection and delivery service. It should not advertise a downloadable Just Eat scraper or instant self-service tool unless NenoData separately verifies one.
Frequently asked questions
What is Just Eat data scraping?
Just Eat data scraping is the structured collection of approved restaurant, menu, price, promotion, delivery-context, rating, location, and timestamp fields from agreed Just Eat marketplace sources.
What Just Eat restaurant fields can be collected?
Potential fields include restaurant identity, address/location, postcode/search context, cuisine/category, minimum order, visible fee fields, availability, rating, review count, ETA context, source URL, and collection timestamp.
Final fields are confirmed against representative pages.
Can NenoData collect complete Just Eat menu data?
Menu categories, items, descriptions, prices, variants, modifiers, promotions, and availability can be scoped where the approved page exposes them.
Do not interpret “complete” as guaranteed access to every hidden modifier or option.
Why does postcode matter?
Just Eat's UK marketplace uses location to determine nearby restaurants and delivery context. The postcode or equivalent location input should remain attached to each observation.
Which price components can be monitored?
Depending on visibility and scope: listed item price, starting/from price, modifier price, promotions, delivery fee, service fee, small-order fee, and minimum-order information.
Can prices be monitored over time?
Yes, where recurring collection is feasible and agreed. Keep restaurant/item matching fields and timestamps with every observation.
Can delivery fees and minimum orders be included?
Yes where those fields are publicly displayed and confirmed during sample review.
Does the displayed delivery fee equal the final checkout price?
Not necessarily. Keep visible components separate unless a final checkout total is separately observed and approved.
Can ratings and reviews be included?
Public ratings and review counts may be included where visible. Individual review text is not guaranteed.
How does restaurant extraction differ from menu extraction?
Restaurant-level extraction focuses on restaurant identity, location, cuisine, reputation, and delivery context. Menu-level extraction adds categories, items, item prices, variants, modifiers, promotions, and item availability.
Can NenoData cover every UK restaurant?
Do not assume universal coverage. Target postcodes, locations, restaurant sets, and source states should be validated during scoping.
Is the data real time?
No universal real-time promise should be made. Freshness depends on the approved scope and agreed collection cadence.
Can menu prices be checked every hour?
Hourly monitoring can be requested, but the cadence should only be committed after source and volume feasibility are confirmed.
Can Just Eat and Deliveroo be combined?
A combined project can be evaluated once Just Eat feasibility and cross-platform restaurant/item matching requirements are confirmed.
Can Just Eat be collected across Europe?
Not as a blanket claim. Each country, brand/domain, location model, and source structure requires individual feasibility review.
Can customer order history be included?
No private or account-protected customer order history should be included in this public-marketplace service.
What happens when menus change?
Preserve raw source fields, identity keys, timestamps, and explicit match/exception states. Do not automatically treat ambiguous renamed items as the same product.
What outputs are available?
CSV, Excel, JSON, API-ready records, scheduled feeds, and cloud/database delivery may be scoped where technically feasible.
Is this a self-service Just Eat scraper?
No self-service scraper should be implied. Position the offering as a managed NenoData workflow unless a separate tool is verified.
Can we request a sample?
Yes. Start with the source, locations, fields, and intended output so feasibility can be reviewed before production.
Request a Just Eat UK data sample around your restaurants and postcodes
Share the UK restaurants or chains you want to monitor, target postcodes or location inputs, menu depth, pricing and delivery fields, required refresh cadence, and preferred output.
NenoData can review the approved Just Eat source, validate representative fields, align an illustrative schema, and confirm what is feasible before production.
Include:
- Target Just Eat country/domain
- UK postcodes, cities, or location inputs
- Restaurant/chain list
- Restaurant- or menu-level depth
- Modifier requirements
- Price/promotion fields
- Delivery/service/small-order fees
- Minimum-order fields
- Rating/review requirements
- Refresh requirement
- Output/destination
- Just Eat-only or multi-platform scope